基于共享度的上海郊区社区居民活动空间隔离及其影响因素

doi:10.11821/dlxb202004013

地理学报 ›› 2020, Vol. 75 ›› Issue (4): 849-859.doi: 10.11821/dlxb202004013

基于共享度的上海郊区社区居民活动空间隔离及其影响因素

塔娜^1,², 申悦^3,⁴()

1. 华东师范大学地理信息科学教育部重点实验室,上海 200241
2. 华东师范大学地理科学学院,上海 200241
3. 华东师范大学中国行政区划研究中心,上海 200062
4. 华东师范大学中国现代城市研究中心,上海 200062

收稿日期:2018-11-27 修回日期:2019-12-22 出版日期:2020-04-25 发布日期:2020-06-25
作者简介:塔娜(1986-), 女, 内蒙古包头人, 博士, 副研究员, 研究方向为城市社会地理学与行为地理学。E-mail: nta@geo.ecnu.edu.cn
基金资助:
国家自然科学基金项目(41971200);国家自然科学基金项目(41871166);中央高校基本科研业务费项目(2018ECNU-QKT001)

Activity space-based segregation among neighbors and its influencing factors: An analysis based on shared activity spaces in suburban Shanghai

TA Na^1,², SHEN Yue^3,⁴()

1. Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China
2. School of Geographic Sciences, East China Normal University, Shanghai 200241, China
3. Research Center for China Administrative Division, East China Normal University, Shanghai 200062, China
4. The Center for Modern Chinese City Studies, East China Normal University, Shanghai 200062, China

Received:2018-11-27 Revised:2019-12-22 Published:2020-04-25 Online:2020-06-25
Supported by:
National Natural Science Foundation of China(41971200);National Natural Science Foundation of China(41871166);Fundamental Research Funds for the Central Universities(2018ECNU-QKT001)

摘要/Abstract

摘要：

在流动性不断增强与居民活动模式日益多样化的背景下,城市社会空间分异与隔离的内涵不断扩展,居民在日常生活中面临的空间隔离受到关注。以活动空间为基础的社会空间分异研究快速兴起,以期从更加动态的视角理解城市社会空间。基于上海市郊区问卷调查数据,构建活动空间共享度指标,建立个体尺度的活动空间分异指数和孤立指数,刻画社区内不同收入群体的活动空间隔离状况,并建立活动空间共享度的多层模型分析活动空间隔离的影响因素。研究发现居住在同一社区的不同收入居民之间存在活动空间隔离,居民更愿意与其社会经济属性相似的人共享活动空间。活动空间隔离程度受到个体社会经济属性、社区设施状况、社区周边环境、社区人口密度和社会混合程度等因素的影响,居住在人口密度高、社会混合度高、社区商业配置良好、公共空间充足的开放社区能提高居民的活动空间共享度,增加不同群体社会接触的可能性。

关键词: 行为地理学, 社会空间, 活动空间, 隔离, 上海

Abstract:

Socio-spatial segregation is an important research topic in urban social geography. Most of the research pays more attention to the residential differentiation and segregation of different groups and proposes that geographical segregation exacerbates the social isolation between disadvantaged groups and other social classes. However, most existing research ignores the spatial differentiation that people suffer from in their daily lives and discusses little about segregation in nonresidential spaces. In the context of increasing mobility, even residents living in the same neighborhood face different degrees of segregation due to differences in the choice of venues, resulting in a lower likelihood of social interaction. Therefore, recent research suggests that it is necessary to pay attention to the differentiation and segregation faced by residents in daily activity spaces and to understand social space from a more comprehensive perspective. However, due to the constraints of data and methods, only a few prior studies have quantitatively measured the activity space segregation of residents living in the same neighborhoods. In particular, there is currently no research focusing on whether policy and planning can effectively reduce activity space segregation. This study intends to solve the problem of socio-spatial segregation in the daily lives of different residents in the same neighborhood through the measurement of activity space. Taking the suburbs of Shanghai as an example, this study analyzes the degree of overlap between residents and other social groups in activity spaces, calculates the "shared activity space" index among individuals, and calculates an individual-scale activity space differentiation index and isolation index on this basis to measure the isolation of different income groups within the community. This indicates that there is indeed segregation between different groups, and the degree of segregation is influenced by individual socio-economic attributes, the mix of residential groups, and the spatial distribution of urban facilities. Living in a neighborhood with high population density, a high social mix, a good community business configuration, and sufficient public space can indeed increase the sharing of residents' activity spaces, while nearby shopping centers will cause isolated activity spaces.

Key words: behavior geography, social space, activity space, segregation, Shanghai

塔娜, 申悦. 基于共享度的上海郊区社区居民活动空间隔离及其影响因素[J]. 地理学报, 2020, 75(4): 849-859.

TA Na, SHEN Yue. Activity space-based segregation among neighbors and its influencing factors: An analysis based on shared activity spaces in suburban Shanghai[J]. Acta Geographica Sinica, 2020, 75(4): 849-859.

图/表 8

表1

图1

表2

表3

图2

图3

表4

表5

活动空间共享度影响因素多层次序logit模型

	模型1：总体活动空间共享度		模型2：低收入居民活动空间共享度		模型3：中收入居民活动空间共享度		模型4：高收入居民活动空间共享度
	系数	P值	系数	P值	系数	P值	系数	P值
收入(低收入为参照)
中收入	-1.34	0.000
高收入	-1.04	0.000
户口(本地户口为参照)
外地城市户口	0.59	0.004	0.97	0.158	0.75	0.006	-0.06	0.887
外地农村户口	0.22	0.357	-0.06	0.915	0.64	0.049	-0.26	0.651
女性	0.19	0.213	0.16	0.717	0.16	0.433	0.09	0.784
年龄	0.00	0.884	0.00	0.855	0.00	0.884	-0.01	0.560
有正式就业	0.44	0.065	0.51	0.264	0.60	0.082	-0.45	0.583
汽车所有权	-0.34	0.045	-0.13	0.750	-0.27	0.242	-0.49	0.263
居住两年以下	-0.29	0.125	0.07	0.882	-0.36	0.156	-0.73	0.071
住房所有权	-0.38	0.061	-0.71	0.189	-0.54	0.049	0.19	0.670
社区有围墙	-2.29	0.023	-1.49	0.339	-1.93	0.078	-4.89	0.024
社区有公共空间	2.63	0.005	4.16	0.005	2.07	0.043	3.82	0.100
社区有室内活动设施	0.49	0.284	-0.72	0.359	0.57	0.263	1.35	0.047
社区有零售商业	0.92	0.022	0.06	0.934	0.78	0.075	1.85	0.002
社区社会混合度	3.86	0.029	1.71	0.533	4.01	0.039	5.06	0.058
社区人口密度	0.30	0.002	0.29	0.058	0.22	0.046	0.60	0.001
周边公园广场密度	0.20	0.797	-1.40	0.326	0.83	0.338	-0.70	0.501
周边购物中心密度	-4.10	0.000	-2.83	0.122	-5.32	0.000	-3.49	0.009
到城市中心的距离	-1.38	0.002	-2.72	0.000	-1.53	0.002	-1.17	0.091
门槛值1	-5.06	0.011	-10.65	0.003	-4.80	0.034	-4.11	0.232
门槛值2	-2.18	0.271	-7.43	0.030	-1.72	0.443	-1.11	0.746
门槛值3	0.37	0.852	-4.85	0.149	0.99	0.661	1.87	0.583
var(_cons)	1.00		1.41		1.06		1.05
Waldchi²	91.63		27.03		53.72		34.38
Loglikelihood	-758.31		-134.01		-432.54		-175.72

表5

参考文献 36

[37]	Song Weixuan, Mao Ning, Chen Peiyang , et al. Coupling mechanism and spatial-temporal pattern of residential differentiation from the perspective of housing prices: A case study of Nanjing. Acta Geographica Sinica, 2017,72(4):589-602. doi: 10.11821/dlxb201704003
	[ 宋伟轩, 毛宁, 陈培阳 , 等. 基于住宅价格视角的居住分异耦合机制与时空特征: 以南京为例. 地理学报, 2017,72(4):589-602.] doi: 10.11821/dlxb201704003
[1]	Domina T . Brain drain and brain gain: Rising educational segregation in the United States, 1940-2000. City & Community, 2006,5(4):387-407.
[2]	Marcuse P, Van Kempen R . Globalizing Cities: A New Spatial Order. Oxford, UK: Blackwell, 2000.
[3]	Reardon S F, Bischoff K . Income inequality and income segregation. American Journal of Sociology, 2011,116(4):1092-1153. doi: 10.1086/657114 pmid: 21648248
[4]	Carter W H, Schill M H, Wachter S M . Polarisation, public housing and racial minorities in US cities. Urban Studies, 1998,35(10):1889-1911.
[5]	Shi Enming, Liu Wangbao, Tang Yiyao . A review of social spatial segregation measurements. Progress in Geography, 2015,34(7):818-829.
	[ 石恩名, 刘望保, 唐艺窈 . 国内外社会空间分异测度研究综述. 地理科学进展, 2015,34(7):818-829.]
[6]	Li Z, Wu F . Tenure‐based residential segregation in post‐reform Chinese cities: A case study of Shanghai. Transactions of the Institute of British Geographers, 2008,33(3):404-419.
[7]	Li Zhigang, Wu Fulong, Xiao Yang . Residential segregation of new migrants in Guangzhou, China: A study of the 6th Census. Geographical Research, 2014,33(11):2056-2068.
	[ 李志刚, 吴缚龙, 肖扬 . 基于全国第六次人口普查数据的广州新移民居住分异研究. 地理研究, 2014,33(11):2056-2068.]
[8]	He Shenjing, Liu Yuting, Wu Fulong , et al. Poverty concentration and determinants in low-income neighbourhoods and social groups in Chinese large cities. Acta Geographica Sinica, 2010,65(12):1464-1475.
	[ 何深静, 刘玉亭, 吴缚龙 , 等. 中国大城市低收入邻里及其居民的贫困集聚度和贫困决定因素. 地理学报, 2010,65(12):1464-1475.]
[9]	Yang S, Wang M Y L, Wang C . Socio-spatial restructuring in Shanghai: Sorting out where you live by affordability and social status. Cities, 2015,47:23-34. doi: 10.1016/j.cities.2014.12.008
[10]	Kempen R V, Özüekren A S . Ethnic segregation in cities: New forms and explanations in a dynamic world. Urban Studies, 1998,35(10):1631-1656.
[11]	Browning C R, Calder C A, Krivo L J , et al. Socioeconomic segregation of activity spaces in urban neighborhoods: Does shared residence mean shared routines. The Russell Sage Foundation Journal of the Social Sciences, 2017,3(2):210-231. doi: 10.7758/RSF.2017.3.2.09 pmid: 29034322
[12]	Park Y M, Kwan M P . Beyond residential segregation: A spatiotemporal approach to examining multi-contextual segregation. Computers, Environment and Urban Systems, 2018,71:98-108.
[13]	Jones M, Pebley A R . Redefining neighborhoods using common destinations: Social characteristics of activity spaces and home census tracts compared. Demography, 2014,51(3):727-752. doi: 10.1007/s13524-014-0283-z pmid: 24719273
[14]	Krivo L J, Washington H M, Peterson R D , et al. Social isolation of disadvantage and advantage: The reproduction of inequality in urban space. Social Forces, 2013,92(1):141-164.
[15]	Zhou Suhong, Yan Xiaopei . The relationship between urban structure and traffic demand in Guangzhou. Acta Geographica Sinica, 2005,60(1):131-142.
	[ 周素红, 闫小培 . 广州城市空间结构与交通需求关系. 地理学报, 2005,60(1):131-142.]
[16]	Chai Yanwei, Shen Jie . Travel-activity based research frame of urban spatial structure. Human Geography, 2006,21(5):108-112, 54.
	[ 柴彦威, 沈洁 . 基于居民移动: 活动行为的城市空间研究. 人文地理, 2006,21(5):108-112, 54.]
[17]	Zhen Feng, Wei Zongcai, Yang Shan , et al. The impact of information technology on the characteristics of urban resident travel: Case of Nanjing. Geographical Research, 2009,28(5):1307-1317.
	[ 甄峰, 魏宗财, 杨山 , 等. 信息技术对城市居民出行特征的影响: 以南京为例. 地理研究, 2009,28(5):1307-1317.]
[18]	Wong D W S, Shaw S L . Measuring segregation: An activity space approach. Journal of Geographical Systems, 2011,13(2):127-145. doi: 10.1007/s10109-010-0112-x pmid: 21643546
[19]	Farber S, O'Kelly M, Miller H J , et al. Measuring segregation using patterns of daily travel behavior: A social interaction based model of exposure. Journal of Transport Geography, 2015,49:26-38. doi: 10.1016/j.jtrangeo.2015.10.009
[20]	Kwan M-P . Beyond space (as we knew it): Toward temporally integrated geographies of segregation, health, and accessibility. Annals of the Association of American Geographers, 2013,103(5):1078-1086.
[21]	Shen Yue, Chai Yanwei . Progress of research on sociospatial differentiation based on daily activity space of urban residents. Progress in Geography, 2018,37(6):853-862.
	[ 申悦, 柴彦威 . 基于日常活动空间的社会空间分异研究进展. 地理科学进展, 2018,37(6):853-862.]
[22]	Chapin Jr F S . Activity systems and urban structure: A working schema. Journal of the American Institute of Planners, 1968,34(1):11-18.
[23]	Hägerstrand T . What about people in regional science? Papers in Regional Science, 1970,24(1):7-24. doi: 10.1111/j.1435-5597.1970.tb01464.x
[24]	Li F, Wang D . Measuring urban segregation based on individuals' daily activity patterns: A multidimensional approach. Environment and Planning A, 2017,49(2):467-486. doi: 10.1177/0308518X16673213
[25]	Lee J Y, Kwan M-P . Visualisation of socio-spatial isolation based on human activity patterns and social networks in space-time. Tijdschrift voor Economische en Sociale Geografie, 2011,102(4):468-485.
[26]	Farber S, Neutens T, Miller H J , et al. The social interaction potential of metropolitan regions: A time-geographic measurement approach using joint accessibility. Annals of the Association of American Geographers, 2013,103(3):483-504. doi: 10.1080/00045608.2012.689238
[27]	Wang D, Li F, Chai Y . Activity spaces and sociospatial segregation in Beijing. Urban Geography, 2012,33(2):256-277. doi: 10.2747/0272-3638.33.2.256
[28]	Ta Na, Chai Yanwei, Kwan Mei-Po . Suburbanization, daily lifestyle and space-behavior interaction in Beijing. Acta Geographica Sinica, 2015,70(8):1271-1280. doi: 10.11821/dlxb201508007
	[ 塔娜, 柴彦威, 关美宝 . 北京郊区居民日常生活方式的行为测度与空间—行为互动. 地理学报, 2015,70(8):1271-1280.] doi: 10.11821/dlxb201508007
[29]	Bailey N, Besemer K, Bramley G , et al. How neighbourhood social mix shapes access to resources from social networks and from services. Housing Studies, 2015,30(2):295-314.
[30]	He Xiaxu, Liu Pengfei . Research on heterogeneous social structure and neighborhood relationship in urban communities. Human Geography, 2016,31(6):1-9.
	[ 贺霞旭, 刘鹏飞 . 中国城市社区的异质性社会结构与街坊/邻里关系研究. 人文地理, 2016,31(6):1-9.]
[31]	Wang Ning . Reconstruction of neighborhood relationship and building of public space in large residential neighborhoods: A case study in X large residential neighborhood in Shanghai. Journal of Jianghan University (Social Science), 2018,35(2):42-47.
	[ 王宁 . 大型居住社区的公共空间营造与邻里关系重构: 以上海市X大型居住社区为个案. 江汉大学学报(社会科学版), 2018,35(2):42-47.]
[32]	Gan Di, Wang De, Zhu Wei . Research on the residents' commuting feature of large-scale residential district in suburban Shanghai: A case study of Gucun, Baoshan District. Geographical Research, 2015,34(8):1481-1491.
	[ 干迪, 王德, 朱玮 . 上海市近郊大型社区居民的通勤特征: 以宝山区顾村为例. 地理研究, 2015,34(8):1481-1491.]
[33]	Duan Xuehui . Research on community satisfaction of residents in large residential communities. Urban Insight, 2016,41(1):85-95.
	[ 段雪辉 . 大型居住社区居民社区满意度研究. 城市观察, 2016,41(1):85-95.]
[34]	Tribby C P, Miller H J, Brown B B , et al. Comparing the appropriate geographic region for assessing built environmental correlates with walking trips: A comparison using different metrics and model designs. Health & Place, 2017,45:1-9. doi: 10.1016/j.healthplace.2017.02.004 pmid: 28237743
[35]	Zhong Weijing, Wang De . Urban space study based on the temporal characteristics of residents' behavior. Progress in Geography, 2018,37(8):1106-1118.
	[ 钟炜菁, 王德 . 基于居民行为周期特征的城市空间研究. 地理科学进展, 2018,37(8):1106-1118.]
[36]	Dai Dandan, Zhou Chunshan . The spatial-temporal characteristics of middle class daily activities in Guangzhou, China. Human Geography, 2017,32(4):45-53.
	[ 代丹丹, 周春山 . 广州市中产阶层日常活动的时空间特征. 人文地理, 2017,32(4):45-53.]

	基于空间的度量	基于人的度量
背景	城市化背景下中国城市社会分异出现,并通过住房选择在空间中表现出来	社会转型背景下中国城市生活方式差异出现,并通过日常活动的空间选择表现出来
假设	居住在同一社区的居民不存在隔离,社区间的差异带来了社会空间隔离	即使居住在同一社区的居民也可能存在日常生活中的隔离,活动空间中的接触能减少隔离
关注点	住房的空间分选过程	行为动态过程
时空间尺度	中长期、宏观	短期、微观
研究单元	社区或居住空间	活动空间
空间单元测量方法	居住社区、以家为中心的缓冲区	标准椭圆、最小凸多边形、最短路径面积、轨迹缓冲区等
影响因素	社区类型、个体社会经济属性、社区建成环境等	社区建成环境、个体社会经济属性、个体偏好等

	类别	样本量(人)	比例(%)
总样本		748	100
性别	男	388	51.87
	女	360	48.13
教育程度	初中及以下	138	18.45
	高中中专职高	154	20.59
	大专大学	419	56.01
	研究生及以上	37	4.95
年龄(岁)	18~29	178	23.80
	30~39	283	37.83
	40~49	199	26.60
	50~60	88	11.77
户口类型	上海	382	51.07
	外地城市	212	28.34
	外地农村	154	20.59
人均月收入 (元)	≤ 2500	128	17.11
	2501~7500	444	59.36
	≥ 7501	176	23.53

群体差异	群体1占比(%)	群体2占比(%)	活动空间共享度差异(%)	P值
中收入—低收入	31.24	40.62	-9.38	0.000
高收入—低收入	34.75	40.62	-5.87	0.007
高收入—中收入	34.75	31.24	3.51	0.047

变量	意义	均值	标准差
收入	居民平均月收入：1：低收入;2：中收入;3：高收入	2.06	0.02
户口	1：本地户口;2：外地城市户口;3：外地农村户口	1.69	0.03
女性	1：女性;0：男性	0.48	0.02
年龄	单位：岁	37.09	0.36
有正式就业	1：有正式就业;0：无正式就业	0.86	0.01
汽车所有权	1：有;0：无	0.62	0.02
居住两年以下	1：居住两年以下;0：居住两年以上	0.31	0.02
住房所有权	1：有住房所有权;0：无住房所有权	1.36	0.02
社区有围墙	1：有;0：无	0.93	0.01
社区有公共空间	如运动场、小广场等,1：有;0：无	0.93	0.01
社区有室内活动设施	如健身房、棋牌室等,1：有;0：无	0.70	0.02
社区有零售商业	如菜市场、小卖部等,1：有;0：无	0.62	0.02
社区社会混合度	根据六普数据计算社区不同教育水平人群的熵值来衡量社区混合程度,数值越高、社区居民组成混合程度越高	0.44	0.01
社区人口密度	社区人口数量除以社区面积计算,单位：万人/km²	1.91	0.08
周边便利店密度	社区周边3 km范围内便利店密度,单位：个/km²	5.25	0.13
周边餐饮设施密度	社区周边3 km范围内餐饮设施密度,单位：个/km²	48.48	0.95
周边超市密度	社区周边3 km范围内超市密度,单位：个/km²	4.40	0.11
周边公园广场密度	社区周边3 km范围内公园广场密度,单位：个/km²	0.28	0.01
周边购物中心密度	社区周边3 km范围内购物中心密度,单位：个/km²	0.30	0.01
周边室内体育休闲设施密度	社区周边3 km范围内室内体育休闲设施密度,单位：个/km²	8.01	0.20
到城市中心的距离	到人民广场的距离,对数形式	3.11	0.02

基于共享度的上海郊区社区居民活动空间隔离及其影响因素

Activity space-based segregation among neighbors and its influencing factors: An analysis based on shared activity spaces in suburban Shanghai

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 36

相关文章 15

Metrics

本文评价

[1]	吴朝宁, 李仁杰, 郭风华. 基于圈层结构的游客活动空间边界提取新方法[J]. 地理学报, 2021, 76(6): 1537-1552.
[2]	王德, 李丹, 傅英姿. 基于手机信令数据的上海市不同住宅区居民就业空间研究[J]. 地理学报, 2020, 75(8): 1585-1602.
[3]	朱玮, 梁雪媚, 桂朝, 冯永恒, 闫嘉. 上海职住优化效应的代际差异[J]. 地理学报, 2020, 75(10): 2192-2205.
[4]	林晓,徐伟,杜德斌,杨凡. 上海市风险投资企业的空间分布与“技术—资本”地理邻近性[J]. 地理学报, 2019, 74(6): 1112-1130.
[5]	殷杰,许世远,经雅梦,尹占娥,廖邦固. 基于洪涝情景模拟的城市公共服务灾害应急响应空间可达性评价——以医疗急救为例[J]. 地理学报, 2018, 73(9): 1737-1747.
[6]	冯健,钟奕纯. 北京社会空间重构（2000-2010）[J]. 地理学报, 2018, 73(4): 711-737.
[7]	宋伟轩,马雨竹,李晓丽,陈艳如. 南京城市住宅小区房价增长模式与效应[J]. 地理学报, 2018, 73(10): 1880-1895.
[8]	王德,顾家焕,晏龙旭. 上海都市区边界划分——基于手机信令数据的探索[J]. 地理学报, 2018, 73(10): 1896-1909.
[9]	於家, 温家洪, 陈芸, 廖邦固, 杜士强. 基于应急疏散智能体模型模拟的城市避难所空间配置——以上海市静安区为例[J]. 地理学报, 2017, 72(8): 1458-1475.
[10]	谭一洺, 柴彦威, 关美宝. 地理背景的不确定性对时空行为模式分析的影响——基于西宁市的实证研究[J]. 地理学报, 2017, 72(4): 657-670.
[11]	任国平, 刘黎明, 孙锦, 卓东, 袁承程. 基于“胞—链—形”分析的都市郊区村域空间发展模式识别与划分[J]. 地理学报, 2017, 72(12): 2147-2165.
[12]	曹有挥, 梁双波, 吴威, 陈欢, 叶士琳. 枢纽港口城市港航服务业空间组织机理——以上海市为例[J]. 地理学报, 2017, 72(12): 2226-2240.
[13]	塔娜, 柴彦威. 基于收入群体差异的北京典型郊区低收入居民的行为空间困境[J]. 地理学报, 2017, 72(10): 1776-1786.
[14]	石忆邵, 周蕾. 上海市地下商业空间使用权估价及空间分异[J]. 地理学报, 2017, 72(10): 1787-1799.
[15]	刘云刚, 苏海宇. 基于社会地图的东莞市社会空间研究[J]. 地理学报, 2016, 71(8): 1283-1301.